Mining subsidence residual deformation prediction method and system based on logarithmic function model
Through the logarithmic function model and nonlinear iterative algorithm, the problem of poor model complexity and real-time performance in the prediction of residual deformation of ground settlement caused by underground mining is solved, and high-precision and space-time prediction are achieved, which improves the accuracy and real-time prediction of predictions. .
Patent Information
- Application Number
- CN202411416223.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-10-11
- Publication Date
- 2025-01-03
AI Technical Summary
When the prior art predicts residual deformation of ground settlement caused by underground mining, the model is complex, the required parameters are many, the prediction accuracy is low, and the real-time performance is poor. It is impossible to quickly and accurately obtain the key factors of dynamic changes, resulting in inaccurate prediction results.
Using a method based on logarithmic function model, model parameters are determined through monitoring point data collection and nonlinear iterative algorithm, residual settlement deformation quantity, rate and acceleration curve are established, model parameters are updated in real time to improve prediction accuracy, and space-time dual prediction is realized.
It significantly improves prediction accuracy, can quickly respond to the latest data, adjust the model in real time, accurately reflect the surface deformation process, overcome the problem of poor real-time performance in traditional methods, and achieve high-precision short-term prediction and accurate prediction of threshold conditions.
Smart Images
Figure CN119245601A_ABST
Abstract
Description
Technical Field
[0001] The invention relates to the technical field of ground subsidence residual deformation prediction, and is particularly applicable to a mining subsidence residual deformation prediction method and system based on a logarithmic function model. Background Art
[0002] The ground collapse problem caused by underground mineral mining has always been a hot and difficult problem in the fields of mining engineering, geotechnical engineering, and earth science. As the underground working face continues to be mined, the deformation of the rock strata in the caving zone, broken zone, and curved zone is transmitted to the surface, which in turn causes surface settlement deformation. The surface mining subsidence deformation process can be divided into two significantly different stages, namely the initial violent deformation period and the subsequent residual settlement deformation period. The residual settlement deformation period occurs for a long time after the end of mining. During the residual settlement deformation period, the ground will produce continuous settlement deformation, affecting the land redevelopment and utilization cycle such as land reclamation, ecological restoration, and new projects. Therefore, scientific prediction, effective prevention and proper disposal of mining subsidence residual settlement deformation are of great significance to ensure mine safety, protect the ecological environment, maintain social stability, and promote the sustainable development of the mining industry.
[0003] At present, the academic community mainly uses probability integral method, limit equilibrium method and finite element method to study the residual deformation of mining subsidence. However, on the one hand, the above methods require a detailed description of the three-dimensional model of the overlying rock and soil and the mining working face, and need to comprehensively consider the comprehensive influence of multiple variables such as geological structure, rock and soil characteristics, and mining technology. On the other hand, the above methods are based on simplified mechanical models and assumptions, and do not fully consider the nonlinearity, anisotropy and other complex factors of actual geological conditions, resulting in a large discrepancy between the prediction results and the actual situation; at the same time, the above methods rely heavily on theoretical calculations to predict the residual deformation of mining subsidence, and the parameters involved are often set based on measured data, such as the compression coefficient of the soil, elastic modulus, Poisson's ratio, groundwater level changes, etc., which are themselves affected by measurement errors and site specificity, and are difficult to obtain accurately in some cases.
[0004] As mining activities progress, some key factors affecting surface deformation may change dynamically, challenging the validity of the original parameters. However, the above methods cannot obtain all accurate parameters for rapid prediction in a short period of time, which hinders the timely and effective prediction of surface subsidence. Summary of the invention
[0005] The purpose of the present invention is to provide a method and system for predicting residual deformation of mining subsidence based on a logarithmic function model, which is used to solve the problems of complex model, multiple required parameters, low prediction accuracy and poor real-time performance in the current prediction of residual deformation of surface mining subsidence.
[0006] To achieve the above object, the present invention adopts the following technical solutions: The mining subsidence residual deformation prediction method based on the logarithmic function model of the present invention comprises the following steps: S1, collects data at monitoring points, including the cumulative monitoring time t and cumulative settlement δ after the period of severe surface deformation; S2, establish a logarithmic function model, select a period of continuous monitoring time interval settlement data, use a nonlinear iterative algorithm to determine the parameters of the logarithmic function model, and obtain a prediction model, which is the residual settlement deformation curve of the monitoring point; S3, determining the cumulative settlement corresponding to any cumulative detection time according to the residual settlement deformation curve; S4, obtaining a residual settlement deformation rate curve and a residual settlement deformation acceleration curve according to the cumulative settlement amount corresponding to the cumulative detection time.
[0007] Furthermore, the units of the cumulative monitoring time t include hours, days, weeks, and months.
[0008] Furthermore, the collected data include the effective cumulative monitoring time t and the cumulative settlement δ after no less than three consecutive months and more than 12 periods of severe surface deformation.
[0009] Furthermore, the settlement data currently collected at the monitoring points are used for the nonlinear iterative calculation of S2, and the model parameters are updated in real time to improve the prediction accuracy.
[0010] The mining subsidence residual deformation prediction system based on the logarithmic function model of the present invention adopts the mining subsidence residual deformation prediction method based on the logarithmic function model, including a data entry module, a parameter setting module, a model calculation module and an output result module; The data entry module is used to import and save the txt file of the cumulative monitoring time t and the cumulative settlement δ after the severe surface deformation period; The parameter setting module is used to obtain the starting date of the prediction system, the critical index of settlement, and the data interval involved in the model calculation; The model calculation module determines the logarithmic function model parameters using a nonlinear iterative algorithm based on the data involved in the model calculation; The output result module outputs the residual settlement deformation curve, the residual settlement deformation rate curve, and the residual settlement deformation acceleration curve of the monitoring point according to the logarithmic function model and the settlement data currently collected at the monitoring point.
[0011] Furthermore, according to the critical index of the settlement, the output result module outputs a date which can identify the date corresponding to the first time the settlement reaches the critical index from the starting date.
[0012] The advantage of the present invention is that it uses nonlinear curve fitting technology to capture and express the complex patterns of various types of surface residual settlement deformations changing over time. The prediction model of the present invention has the ability to update and correct in real time, and can continuously adjust its internal parameters according to the latest collected monitoring data to ensure that the simulated deformation curve closely fits the ground deformation conditions observed in the field, thereby significantly improving the accuracy of the prediction, so that the model can more sensitively and accurately reflect the actual evolution of surface deformation during the mining process, and effectively overcome the prediction error problem caused by the poor real-time performance of model parameters in traditional methods. In addition to achieving high-precision prediction of residual settlement in a short period of time, the present invention can also accurately estimate the specific time when the residual settlement deformation first reaches the set threshold conditions, realizing dual prediction functions of time and space. BRIEF DESCRIPTION OF THE DRAWINGS
[0013] Figure 1 The present invention is a flow chart of the method.
[0014] Figure 2 This is a schematic diagram of the monitoring data format requirements in the method of the present invention.
[0015] Figure 3 Schematic diagram of the system interface of the present invention.
[0016] Figure 4 The figure is a schematic diagram of the final residual settlement deformation result output by the system of the present invention.
[0017] Figure 5 This is a curve diagram of residual settlement deformation calculated by the method of the present invention.
[0018] Figure 6 This is a residual settlement deformation rate curve calculated by the method of the present invention.
[0019] Figure 7 This is a residual settlement deformation acceleration curve calculated by the method of the present invention.
[0020] Figure 8 The nonlinear iteration result output by the system of the present invention is displayed.
[0021] Fig. 9 This is an illustration of the historical data of the monitoring points in Example 3 of the present invention.
[0022] Fig.10 This is a schematic diagram of setting a sedimentation threshold in the system of the present invention.
[0023] Fig.11 It is a schematic diagram of the exact date when the system output of the present invention reaches the sedimentation threshold.
[0024] Fig.12 This is an illustration of setting the daily settling rate threshold in the system of the present invention.
[0025] Fig.13 It is an illustration of the exact date when the system output of the present invention reaches the daily settling rate threshold. DETAILED DESCRIPTION
[0026] The technical solutions in the embodiments of the present invention are described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.
[0027] Example 1 like Figure 1 As shown, the mining subsidence residual deformation prediction method based on the logarithmic function model of the present invention comprises the following steps: S1, collect data at monitoring points, including the cumulative monitoring time t and cumulative settlement δ after the period of severe surface deformation; the units of cumulative monitoring time t include hours, days, weeks, and months. To ensure the accuracy of model prediction, the collected data should include the effective cumulative monitoring time t and cumulative settlement δ after no less than three consecutive months and more than 12 periods of severe surface deformation.
[0028] S2, establish a logarithmic function model, select a period of continuous monitoring time interval settlement data, use a nonlinear iterative algorithm to determine the parameters of the logarithmic function model, and obtain a prediction model. The prediction model is the residual settlement deformation curve of the monitoring point, which expresses the cumulative settlement δ corresponding to any monitoring time t.
[0029] The logarithmic function model is: Where t is the cumulative monitoring time, δ is the cumulative settlement, and a, b, c, and d are model parameters. The values of a, b, c, and d need to be determined by a nonlinear iterative algorithm using the collected and observed settlement data.
[0030] In one embodiment, monitoring data of a specific monitoring interval can be selected to participate in the fitting calculation of the logarithmic model. The specific monitoring interval needs to be a continuous monitoring time interval. The remaining monitoring data can be used as a validation set to evaluate the prediction performance of the logarithmic model determined by the fitting.
[0031] In another embodiment, the monitoring data of all monitoring intervals may also be selected to participate in the fitting calculation of the logarithmic model.
[0032] S3, determining the residual settlement deformation corresponding to any predicted time point according to the residual settlement deformation curve.
[0033] For the residual settlement deformation curve, when t tends to infinity, the asymptote of the residual settlement deformation curve is the final residual settlement deformation of the monitoring point. According to the residual settlement deformation curve, it can be known that the cumulative settlement corresponding to any predicted time point minus the cumulative settlement corresponding to the current time point is the residual settlement deformation of the monitoring point at the predicted time point. In this way, the residual settlement deformation of the surface at a certain time in the future can be accurately calculated.
[0034] S5, obtaining a residual settlement deformation rate curve and a residual settlement deformation acceleration curve according to the cumulative settlement amount corresponding to the monitoring time.
[0035] The residual settlement deformation rate curve expresses the curve of the cumulative settlement rate and time. The residual settlement deformation acceleration curve expresses the curve of the change rate of the cumulative settlement and time. The residual settlement deformation rate curve can be used to know the daily deformation of the surface. The residual settlement deformation acceleration curve can be used to know when the surface residual settlement deformation is faster and when it tends to be stable. These data can be used to scientifically evaluate the stability of surface settlement, provide guidance for the reasonable planning and construction safety of various engineering construction projects, and effectively help engineering decision makers adjust construction plans in real time and in a timely manner to ensure engineering safety and quality, while also being beneficial to environmental protection and efficient use of resources.
[0036] Furthermore, the creativity of the present invention lies in that the settlement data currently collected at the monitoring point can be used in real time for the nonlinear iterative calculation of S2, thereby being able to update the model parameters in real time and improve the prediction accuracy.
[0037] Example 2 The mining subsidence residual deformation prediction system based on the logarithmic function model of the present invention adopts the mining subsidence residual deformation prediction method based on the logarithmic function model to provide users with a mining subsidence residual deformation prediction platform, which specifically includes a data entry module, a parameter setting module, a model calculation module and an output result module.
[0038] The data entry module is used to import a txt file that stores the cumulative monitoring time t and the cumulative settlement δ after the severe surface deformation period. The first column in the txt file is the cumulative monitoring time t, and the second column is the cumulative settlement δ. The first column and the second column are separated by a comma and no space.
[0039] The parameter setting module is used to obtain the starting date of the prediction system, the critical index of settlement, and the data interval involved in the model calculation.
[0040] The starting date is used to determine the starting time of the output data of the prediction model. According to the specific date of the starting time, the mining subsidence residual deformation prediction system based on the logarithmic function model of the present invention can combine the output of the prediction model and the set settlement critical index to timely present the exact date when the surface settlement first reaches the critical index starting from the starting date.
[0041] The critical indicators of settlement include cumulative settlement, daily settlement, and settlement acceleration. The data interval involved in the model calculation determines which data in the data txt file is used for nonlinear iterative calculation.
[0042] The model calculation module determines the logarithmic function model parameters using a nonlinear iterative algorithm based on the data involved in the model calculation.
[0043] The output result module outputs the residual settlement deformation curve, residual settlement deformation rate curve, and residual settlement deformation acceleration curve of the monitoring point according to the logarithmic function model and the settlement data currently collected at the monitoring point. According to the settlement critical index, the output result module can identify the date corresponding to the first time the settlement reaches the critical index from the starting date, so as to more intuitively and clearly analyze and grasp the settlement changes in the area.
[0044] Example 3 Taking the measured data of a ground subsidence monitoring point in a coal mine as an example, the implementation steps of the present invention and its prediction results are explained in detail.
[0045] Step 1: The monitoring data of the monitoring points are Figure 2 The format is processed, the basic format is as follows: Cumulative monitoring time, cumulative settlement displacement Cumulative monitoring time, cumulative settlement displacement Cumulative monitoring time, cumulative settlement displacement .... The monitoring point was on the 1st day on March 01, 2020, with a settlement displacement of 0. On June 25, 2021, it was the 481st day, with a settlement displacement of 106.6, for a total of 13 monitoring data.
[0046] Step 2: If Figure 3 As shown, in the mining subsidence residual deformation prediction system based on the logarithmic function model, find the txt file through the data file browser, select this file and click to open. Click to set the starting date and enter "20200301". Select "Use all data" as the data interval involved in the model calculation. In this embodiment, the critical index of settlement is not set.
[0047] Step 3: Start calculation. The model calculation module will automatically determine the logarithmic function model parameters based on the data involved in the model calculation using a nonlinear iterative algorithm. Then, based on the obtained prediction model, let the monitoring time variable t in the model tend to infinity, and obtain the value corresponding to the asymptote of the residual settlement deformation curve, which is the final residual settlement deformation of the monitoring point. The final residual settlement deformation value is displayed in the text box corresponding to "Final residual settlement deformation" in the calculation results. Figure 4 As shown, after calculation, the final cumulative residual settlement deformation of the monitoring point in this embodiment is 135.4 cm.
[0048] At the same time, the mining subsidence residual deformation prediction system based on the logarithmic function model will generate a folder of output + generation time after the calculation is completed, which stores the residual settlement deformation curve (such as Figure 5 As shown in the figure), residual settlement deformation rate curve (as shown in the figure Figure 6 As shown in the figure), residual settlement deformation acceleration curve (as shown in the figure Figure 7 As shown in the figure, there are two files: output.txt and outputdata.txt. In output.txt, we can see the logarithmic function model parameters determined by nonlinear iterative algorithm, that is, the values of a, b, c, d, as well as the residuals, average residuals, correlation coefficients, and asymptotes of the prediction model. Figure 8 The correlation coefficient R=0.997 shows the degree of consistency between the prediction model and the measured data. The curve of the later monitoring data is the prediction curve, and the asymptote Line=135.4 obtained by the asymptotic trend of the curve is the final cumulative settlement value of the monitoring point. Fig. 9 The outputdata.txt file shown contains the historical data of the monitoring point observation nodes, including the cumulative settlement displacement, daily settlement rate and settlement acceleration. The figure shows that the last measured data of the monitoring point is 106.6 cm, and the corresponding date is June 24, 2021. Therefore, after June 24, 2021, there is still a residual settlement deformation of 135.4-106.6=28.8 cm until the final settlement stabilizes.
[0049] Through the residual settlement deformation curve (such as Figure 5 As shown in the figure), residual settlement deformation rate curve (as shown in the figure Figure 6 As shown in the figure), residual settlement deformation acceleration curve (as shown in the figure Figure 7 (as shown) can intuitively display other settlement and deformation characteristics of the monitoring point.
[0050] Step 3: After obtaining the final cumulative settlement deformation of the monitoring point, the project also focuses on the time to complete the settlement and reach settlement stability. In the mining subsidence residual deformation prediction system based on the logarithmic function model, the settlement critical index is set, such as Fig.10 As shown in the figure, "Set the settlement amount 135.4 as the threshold value". Start the calculation again. After the calculation is completed, the exact date when the settlement amount is reached is displayed in the "Date that meets the set threshold conditions" corresponding to the "Set settlement amount threshold" in the calculation results, such as Fig.11 shown.
[0051] The basic principle is to substitute the set cumulative settlement of 135.4 into the determined prediction model, that is, , we can get the time t corresponding to the completion of the cumulative settlement. Based on the starting date 20200301, we can calculate that the date that meets the set threshold conditions is May 29, 2040.
[0052] Step 4: In the field of engineering practice, the residual settlement deformation of the ground surface is a gradual and long process. Usually, once the daily settlement rate drops below the preset safety threshold, even if the complete settlement stability state is not reached at this time, it will not affect the construction of the project. This is because after appropriate basic treatment measures, even if there is continuous trace settlement, the safety and stability of the engineering structure can still be guaranteed. This method balances the project progress and safety requirements, and avoids unnecessary delays that may be caused by waiting for absolute stability. Therefore, the present invention also provides a daily settlement rate prediction function.
[0053] In the threshold setting module of the mining subsidence residual deformation prediction system based on the logarithmic function model, the critical index of settlement is set, such as Fig.12 As shown in the figure, "Set the daily sedimentation rate as 0.01 as the threshold value", click to start the calculation. After the calculation is completed, the "Date that meets the set threshold conditions" corresponding to the "Set daily sedimentation rate threshold" in the calculation results will show the exact date when the set daily sedimentation rate threshold is reached, such as Fig.13 shown.
[0054] The basic principle is to substitute the set daily sedimentation rate of 0.01 into the rate equation (The first derivative of the settlement displacement-time function) can be used to obtain the time t corresponding to the settlement rate of the day. Based on the initial date 20200301, it can be calculated that the date that meets the set threshold condition is May 25, 2025. The corresponding results can also be viewed in the output.txt file output by the mining subsidence residual deformation prediction system based on the logarithmic function model.
[0055] The mining subsidence residual deformation prediction method and system based on the logarithmic function model in the present invention is based on abundant measured data. By deeply mining and finely processing a large amount of mining subsidence field monitoring data, a logarithmic model describing the surface residual settlement deformation characteristics is extracted. The model has the following significant advantages: Real-time dynamic update: It can quickly respond to the latest daily monitoring data, adjust and optimize fitting parameters in real time, and instantly generate fitting curves that reflect the latest deformation status, ensuring accurate control of deformation dynamic trends and achieving high-precision prediction and real-time forecasting.
[0056] Dual prediction in time and space: In addition to accurately estimating the final total residual deformation of the surface, it also uniquely provides the ability to predict in the time dimension, that is, to clearly indicate when the surface deformation will reach the safety threshold set by the project. The accurate prediction of this time point is crucial for key links such as engineering design, construction scheduling, and maintenance planning, and helps project managers to formulate response strategies in a forward-looking manner to ensure the safe and efficient progress of engineering construction.
[0057] Fast, efficient and easy to operate: The present invention cleverly avoids the need for complex theoretical calculation parameters and only relies on the inherent mechanism of the model to process the accurate settlement data acquired in real time, thereby quickly generating prediction results, showing the advantages of simplicity and practicality.
[0058] In summary, the mining subsidence residual deformation prediction method based on the logarithmic function model provided by the present invention provides strong technical support for engineering builders with its real-time data response mechanism, unique spatiotemporal prediction capabilities, and fast, efficient, and easy-to-operate technical advantages, and greatly improves the surface deformation management efficiency and decision-making accuracy in underground mining activities.
Claims
1. A mining subsidence residual deformation prediction method based on a logarithmic function model, characterized in that: The following steps are involved: S1, collects data at monitoring points, including the cumulative monitoring time t and cumulative settlement δ after the period of severe surface deformation; S2, establish a logarithmic function model, select a period of continuous monitoring time interval settlement data, use a nonlinear iterative algorithm to determine the parameters of the logarithmic function model, and obtain a prediction model, which is the residual settlement deformation curve of the monitoring point; S3, determining the cumulative settlement corresponding to any cumulative detection time according to the residual settlement deformation curve; S4, obtaining a residual settlement deformation rate curve and a residual settlement deformation acceleration curve according to the cumulative settlement amount corresponding to the cumulative detection time.
2. The method for predicting residual deformation of mining subsidence based on a logarithmic function model according to claim 1 is characterized in that: The units of the cumulative monitoring time t include hours, days, weeks and months.
3. The method for predicting residual deformation of mining subsidence based on a logarithmic function model according to claim 1 is characterized in that: The collected data include the effective cumulative monitoring time t and cumulative settlement δ after no less than three consecutive months and more than 12 periods of severe surface deformation.
4. The mining subsidence residual deformation prediction method based on the logarithmic function model according to claim 1 is characterized in that: The settlement data currently collected at the monitoring point is used for the nonlinear iterative calculation of S2, and the model parameters are updated in real time to improve the prediction accuracy.
5. A mining subsidence residual deformation prediction system based on a logarithmic function model, characterized by: A mining subsidence residual deformation prediction method based on a logarithmic function model as described in any one of claims 1 to 4, comprising a data entry module, a parameter setting module, a model calculation module and an output result module; The data entry module is used to import and save the txt file of the cumulative monitoring time t and the cumulative settlement δ after the severe surface deformation period; The parameter setting module is used to obtain the starting date of the prediction system, the critical index of settlement, and the data interval involved in the model calculation; The model calculation module determines the logarithmic function model parameters using a nonlinear iterative algorithm based on the data involved in the model calculation; The output result module outputs the residual settlement deformation curve, the residual settlement deformation rate curve, and the residual settlement deformation acceleration curve of the monitoring point according to the logarithmic function model and the settlement data currently collected at the monitoring point.
6. The mining subsidence residual deformation prediction system based on the logarithmic function model according to claim 5 is characterized by: According to the critical index of the settlement, the output result module outputs a date which can identify the date corresponding to the first time the settlement reaches the critical index from the starting date.